Map-making for the Next Generation of Ground-based Submillimeter Instruments

TL;DR

This paper discusses the challenges of creating sky maps from large, complex datasets generated by next-generation ground-based submillimeter instruments, and proposes strategies to handle the data effectively.

Contribution

It introduces new strategies for scaling map-making techniques to handle the massive data volumes of future submillimeter instruments.

Findings

Current iterative methods are effective for existing instruments.

Scaling to larger datasets requires new data reduction strategies.

Proposed strategies aim to manage data size and complexity.

Abstract

Current ground-based submillimeter instruments (e.g. SCUBA-2, SHARC-2 and LABOCA) have hundreds to thousands of detectors, sampled at tens to hundreds of hertz, generating up to hundreds of gigabytes per night. Since noise is correlated between detectors and in time, due to atmospheric signals and temperature oscillations, naive map-making is not applicable. In addition, the size of the data sets makes direct likelihood based inversion techniques intractable. As a result, the data reduction approach for most current submm cameras is to adopt iterative methods in order to separate noise from sky signal, and hence effectively produce astronomical images. We investigate how today's map-makers scale to the next generation of instruments, which will have tens of thousands of detectors sampled at thousands of hertz, leading to data sets of challenging size. We propose strategies for reducing such large data sets.